Atmospheric Turbidity Research Articles

Atmospheric Turbidity of Solar Radiation over Langtang National Park

The main aim of this project is to study atmospheric turbidity of solar radiation over Langtang National Park (28.2112° N, 85.5663° E and altitude 3862 m a.s.l.). The daily aerosol optical depth (AOD) data are derived from Aerosol robotic network (AERONET) for a period of one year 2018. Annual mean of Angstrom exponential (α), Angstrom turbidity coefficient (β) and curvature of AOD (a2) are found 1.04 ± 0.29, 0.03± 0.02 and 4.1 ± 0.9 respectively. The average value of and Linke turbidity (LT) is 2.5 ± 0.6. Result of this research work is beneficial for the further identification, impact and analysis of atmospheric turbidity factors at different places.

Combing remote sensing information entropy and machine learning for ecological environment assessment of Hefei-Nanjing-Hangzhou region, China

Urban ecological environment is the basis of citizens’ survival and development. A rapid and objective urban ecological environment assessment (UEEA) plays an important role in the urban sustainable development and environment protection. This study established an improved urban ecological comfort index (UECIIMP), which is based on our previous UECI and fully composed of four remote sensing indicators: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), land surface temperature (LST), and aerosol optical depth (AOD), representing the greenness, dryness, heat, and atmospheric turbidity, respectively. Combining the entropy method and random forest (RF) algorithm, the weights of four indicators were calculated. To improve the accuracy of UECIIMP, the gap-filled quarterly mean results of each indicator with 30m resolution were obtained using the harmonic analysis of time series (HANTS) method and spatial-temporal information fusion based on non-local means filter (STNLFFM). UECIIMP was applied to the Hefei-Nanjing-Hangzhou Region to explore its spatiotemporal changes and response characteristics. Results show that the weights of UECIIMP fluctuate slightly (within 10%) before and after sensitivity analysis, with good stability and reliability. UECIIMP in Hangzhou > Hefei ≈ Nanjing, spring ≈ autumn > summer ≫ winter. From 2009 to 2019, UECIIMP has improved in all 33 districts of the Hefei-Nanjing-Hangzhou Region. The significant improvement of UECIIMP in 2014–2019 is 4.3 times than that in 2009–2014. The correlation between UECIIMP and economic index indicates that economic development has a positive impact on the urban ecological environment. The significant degradation of UECIIMP in the urban expansion area demonstrates a negative impact on the local environment from urban expansion.

Retrieving nighttime aerosol optical depth using combined measurements of satellite low light channels and ground-based integrating spheres

ABSTRACT The aerosol optical depth, as an aerosol optical property, is a key physical quantity used to characterize the degree of atmospheric turbidity. The radiance of the integrating sphere light source after radiometric calibration is relatively stable. Compared with the original use of city lights to invert the night-time aerosol optical depth (AOD), the use of the integrating sphere can effectively reduce the change of the emission source and improve the inversion accuracy. A model to retrieve night-time AOD based on combined measurements of satellite low light channels and ground-based integrating spheres was established by the night-time artificial observation experiment in the Dunhuang radiation correction field (DRCF) for 38 consecutive days from October to November 2018. After excluding cloud pollution and moonlight pollution, the calculated results are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) data and the Visible/Infrared Imaging Radiometer Suite (VIIRS) night-time aerosol data. The average root mean square error of the processed data is 0.11 and 0.23, and the correlation coefficient reaches 0.85 and 0.46, respectively. The experimental results preliminarily show that the retrieval AOD is in good agreement with the satellite data, and it is feasible to retrieve AOD based on combined measurements of satellite low light channels and ground-based integrating spheres.

A remote sensing assessment index for urban ecological livability and its application

ABSTRACT Remote sensing provides us with an approach for the rapid identification and monitoring of spatiotemporal changes in the urban ecological environment at different scales. This study aimed to construct a remote sensing assessment index for urban ecological livability with continuous fine spatiotemporal resolution data from Landsat and MODIS to overcome the dilemma of single image-based, single-factor analysis, due to the limitations of atmospheric conditions or the revisit period of satellite platforms. The proposed Ecological Livability Index (ELI) covers five primary ecological indicators – greenness, temperature, dryness, water-wetness, and atmospheric turbidity – which are geometrically aggregated by non-equal weights based on an entropy method. Considering multisource time-series data of each indicator, the ELI can quickly and comprehensively reflect the characteristics of the Ecological Livability Quality (ELQ) and is also comparable at different time scales. Based on the proposed ELI, the urban ecological livability in the central urban area of Wuhan, China, from 2002 to 2017, in the different seasons was analyzed every 5 years. The ELQ of Wuhan was found to be generally at the medium level (ELI ≈0.6) and showed an initial trend of degradation but then improved. Moreover, the ecological livability in spring and autumn and near rivers and lakes was found to be better, whereas urban expansion has led to the outward ecological degradation of Wuhan, but urban afforestation has enhanced the environment. In general, this paper demonstrates that the ELI has an exemplary embodiment in urban ecological research, which will support urban ecological protection planning and construction.

Longtime solar performance estimations of low-E glass depending on local atmospheric conditions

In the construction industry, low-emissivity glass is used to improve both thermal efficiency and visual comfort by reflecting or transmitting thermal radiation. The solar optical performances of this type of glass are calculated using the ASTM G173 (AM 1.5) solar spectrum, the unique worldwide standard. However, the local atmospheric conditions of real sites around the world are different from those used to model the ASTM solar spectrum, originally created for PV deployment in North America in the 2000s. As the latter has an impact on the shape of the ground-based solar spectra, the real solar properties of the installed low-e glass can be different from those calculated in laboratory conditions and using the standard. In this paper, we provide a comprehensive, multi-years analysis of the solar performances of a complete set of low-e glass located on 12 sites around the world. Local solar spectra are modelled using local atmospheric data. This new study analyse the solar transmittance of low-e glass around the world. Our results show that the ASTM solar spectrum is appropriate for the majority of the selected locations. For some particular sites, such as buildings near the equator or located in cities with high atmospheric turbidity, the standard solar spectrum is not appropriate. For these sites, solar transmittance can vary up to 5% when compared to the ASTM standard. Consequently, this paper shows the necessity to include the impact of local atmospheric conditions on the performances of low-e glass.

Spatial and temporal distribution of satellite turbidity in response to different environmental variables in the Bahía Blanca Estuary, South-Western Atlantic

ABSTRACT This work combines in situ measurements with a time series of satellite turbidity derived from Landsat 8-OLI images to provide a first synoptic overview of the main hydrometeorological drivers of turbidity in the Bahía Blanca Estuary (Argentina). An empirical relationship between turbidity and SPM concentrations was established for the study area (R2 = 0.92; RMSE = 0.098 mg m−3; NMAE = 6.2%). Several atmospheric correction schemes and turbidity retrieval algorithms were tested and the combination of the SWIR-v and the retrieval algorithm by Dogliotti et al. (2015) were applied to 121 Landsat 8-OLI scenes (2013–2021). The effects of tides, winds, and rainfall on satellite turbidity were evaluated through Generalized Linear Models (GLM) built for three different sectors along Canal Principal, from the inner zone to its mouth. Regardless of the zone, cumulative rainfall had negligible effects on turbidity. Tides had a significant effect in the inner and middle zones. In the inner zone, higher turbidity values significantly associated with ebb tide conditions, which produce erosion. In the middle section tidal current speeds positively correlated with turbidity, suggesting sediment resuspension over shallow banks. Close to the mouth of the estuary, turbidity responded entirely to winds. Winds blowing from de NW, aligned with the azimuth of Canal Principal, would aid to the export of estuarine sediments to the shelf.

Retrieval of High-Resolution Aerosol Optical Depth for Urban Air Pollution Monitoring

Aerosol Optical Depth (AOD) is one of the most important parameters of aerosol and a key physical quantity to characterize atmospheric turbidity and air pollution. Accurate retrieval of AOD is of great significance for air quality assessment. However, the spatial resolution of the currently widely used Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products is too low to meet the application research of atmospheric environment at the regional scale. In 2013, China launched the Gaofen-1 (GF-1) satellite, which provides a new idea for AOD retrieval. In this paper, we apply the synergetic use of TERRA and AQUA satellite MODIS data to calculate the high-resolution AOD over Beijing based on the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) and discussed scale conversion problems between AODs with different resolutions. To obtain the 100 m MODIS data, we use GF-1 wide-field-of-view data to downscale 1 km MODIS data based on mutual information method. The retrieved AOD has a spatial resolution of 100 m and can cover many land surface types. Preliminary validation was carried out with the Aerosol Robotic Network (AERONET) ground observation data. The correlation coefficient is about 0.88, and the root-mean-square error is about 0.15. Due to the high resolution of retrieved results, more detailed features can be provided in the spatial distribution. The experimental results show that the method has high precision, and further verification work is continuing.

Evaluation of solar radiation models on vertical surface for building photovoltaic applications in Beijing

Building photovoltaic (PV) may provide a growing share of electricity for big cities. However, lack of measured radiation data on vertical surface remains a critical problem for building PV deployment. With the development of urbanization and industrialization, high aerosol load is altering the nature of solar radiation in Beijing. Hence, ten in-plane solar radiation models are reviewed to choose optimal model for calculating solar radiation on vertical surface in such high turbidity area. At the same time, the effect of atmospheric turbidity on the model accuracy is revealed for further understanding on the optimal model. To give general suggestion on model selection, the performance of the ten models for each weather type is tested based on new method of weather type classification. Finally, solar resource on vertical surface and output energy of building PV system are evaluated by the optimal model to guide building PV deployment. The results indicate that Perez model is suitable to calculate solar radiation on vertical surface in high turbidity area. High atmosphere turbidity will cause Perez model inaccurate. Model selection can be made by the status of atmospheric transmittance. It is feasible to apply building PV in Beijing with AC output energy of 630–971 kWh/kWp/year.

Calibration of ground surface albedo models

The emergence of bifacial photovoltaics as a serious option for large-scale power generation makes the accuracy of the in-situ estimation of ground surface albedo increasingly critical. Recent studies have evaluated the performance of different models but an investigation of how these models should be calibrated is missing. This paper proposes geographically generalised parametrisations for time-variant albedo models of vegetated and desert surfaces based on a worldwide database and reports their performance compared to site-specific, unbiased parametrisations. These geographically generalised formulations can be used as an alternative for lengthy calibration campaigns as their only parameters can be easily measured. The paper also presents a simple method for deriving white-sky albedo without the need for diffuse irradiance data. The method is founded both theoretically and empirically. If on-site measurements can be collected to calibrate a model, the duration of the campaign is usually a few weeks at the maximum. This paper first shows evidence of the strong impact of the timing on calibration performance and accordingly gives practical guidelines on the most suitable measurement seasons and weather conditions. In general, early-summer months appear to be the most appropriate time for calibrating most models. Atmospheric turbidity during the campaign period, however, should be either minimised or maximised depending on the site and the model. Finally, in case no flexibility exists in timing the calibration, simple models with few parameters should be deployed. Geometric mean shows the most robust performance at most sites.

Improved turbidity estimation from local meteorological data for solar resourcing and forecasting applications

This work presents a new method to estimate atmospheric turbidity with improved accuracy in estimating clear-sky irradiance. The turbidity is estimated by machine learning algorithms using commonly measured meteorological data including ambient air temperature, relative humidity, wind speed and atmospheric pressure. The estimated turbidity is then served as the Linke Turbidity input to the Ineichen-Perez clear-sky model to estimate clear-sky global horizontal irradiance (GHI) and direct normal irradiance (DNI). When compared with the original Ineichen-Perez model which uses interpolated turbidity from the monthly climatological means, our turbidity estimation better captures its daily, seasonal, and annual variations. When using the improved turbidity estimation in the Ineichen-Perez model, the root mean square error (RMSE) of clear-sky GHI is reduced from 24.02 W m−2 to 9.94 W m−2. The RMSE of clear-sky DNI is deceased from 76.40 W m−2 to 29.96 W m−2. The presented method is also capable to estimate turbidity in partially cloudy days with improved accuracy, evidenced by that the corresponding estimated clear-sky irradiance has smaller deviation from measured irradiance in the cloudless time instants. In sum, the proposed method brings new insights about turbidity estimation in both clear and partially cloudy days, providing support to solar resourcing and forecasting.

Advances in the characterization of aerosol optical properties using long-term data from AERONET in Buenos Aires

We present a complete characterization of aerosols in Buenos Aires based on their optical and microphysical properties from a long-term record (19-years) of AERONET V3 observations at the CEILAP-BA site. The baseline climatology indicates that the city has low mean aerosol load (0.11 ± 0.1) and mean Ångström exponent (α) of 1.11 (±0.4). The long-term trend of the monthly median aerosol optical thickness (AOT) is not significant whereas monthly α medians show a significant negative trend. Atmospheric turbidity increases in August and September (40%). Annual and monthly particle size distributions show predominance of the coarse mode. This apportionment can change under particular conditions, e.g., showing increased fine mode load in August and September.The thresholds of α and AOT proposed for four aerosol types allowed a satisfactory classification of aerosols. The relationship between AOT and α clearly illustrated the major contribution to extinction of finer particles under more turbid conditions. This feature—not evident in the baseline climatology— is consistent with the presence of smoke from regional biomass burning. Sporadic intrusions of coarse particles (dust/ash) from remote sources also enhance extinction. The analysis of the interrelations among aerosol properties (fine mode fraction of AOT, volume concentration, spectral curvature, and size distribution) supports these findings. The aerosols seem to undergo aging and/or hydration processes as shown by the relationships among AOT, extinction due to the fine mode, spectral curvature, and aerosol fine mode radius. This comprehensive analysis revealed details associated with the contributions from local and remote anthropogenic and/or natural sources.

A Hybrid Predicting Model for the Daily Photovoltaic Output Based on Fuzzy Clustering of Meteorological Data and Joint Algorithm of GAPS and RBF Neural Network

Photovoltaic (PV) output is greatly affected by meteorological factors. If it has no efficient meteorological factors, the prediction accuracy for PV is a little low. Although the Radial Basis Function (RBF) network is already widely utilized in photovoltaic prediction, its prediction error is too large. An algorithm for forecasting the evaluation of the short-term PV output based on fuzzy clustering of meteorological data and a joint algorithm of the Genetic Algorithm Programming System (GAPS) and Radial Basis Function (RBF) is proposed in this paper to increase the prediction accuracy. Selecting the three main types of meteorological data, including atmospheric turbidity, relative humidity, and solar irradiance, as clustering feature vectors of the cluster class and clustering that historical PV outputting data into three groups by an improved fuzzy c-means clustering (IFCM) method are significant in this study. Finally, this research implemented the computational simulation for a real case. Its results show that the proposed model and algorithm work well and can reduce the dimension of the model and improve the prediction accuracy.

Estimation of Atmospheric Turbidity Parameters in Ile-Ife, Nigeria

This study estimated the levels of atmospheric turbidity in Ile-Ife, a tropical location in the Southwest of Nigeria, from November, 2017 to March, 2019. This was with the aim to quantify the degree of atmospheric cleanliness of the study location. The methods of estimation used are: the Angstrom turbidity parameters (α and β), Linke turbidity factor (TL) and horizontal visibility (VH). The values of α and β range between 0.6 and 1.4; 0.10 and 0.91 respectively. The values obtained for TL varied between 1 and 7 while visibility values ranged between 2 and 14 km. Maximum values of β and TL (corresponding to low values of VH) were obtained in the dry season (particularly in the months of January and February) while the lowest values of the same methods of estimation (corresponding to high values of VH) were recorded in the wet season (specifically in August and September). The elevated turbidity observed in the dry season was linked to episodes of Harmattan dust storms usually experienced at the study location. The study concluded that a polluted atmosphere dominates the study location especially in the dry season as indicated by the different atmospheric turbidity parameters.

Is the Air Too Polluted for Outdoor Activities? Check by Using Your Photovoltaic System as an Air-Quality Monitoring Device.

Over the past few decades, the concentrating photovoltaic systems, a source of clean and renewable energy, often fully integrated into the roof structure, have been commonly installed on private houses and public buildings. The purpose of those panels is to transform the incoming solar radiation into electricity thanks to the photovoltaic effect. The produced electric power is affected, in the first instance, by the solar panel efficiency and its technical characteristics, but it is also strictly dependent on site elevation, the meteorological conditions and on the presence of the atmospheric constituents, i.e., clouds, hydrometeors, gas molecules and sub-micron-sized particles suspended in the atmosphere that can scatter and absorb the incoming shortwave solar radiation. The Aerosol Optical Depth (AOD) is an adimensional wavelength-dependent atmospheric column variable that accounts for aerosol concentration. AOD can be used as a proxy to evaluate the concentration of surface particulate matter and atmospheric column turbidity, which in turn affects the solar panel energy production. In this manuscript, a new technique is developed to retrieve the AOD at 550 nm through an iterative process: the atmospheric optical depth, incremented in steps of 0.01, is used as input together with the direct and diffuse radiation fluxes computed by Fu–Liou–Gu Radiative Transfer Model, to forecast the produced electric energy by a photovoltaic panel through a simple model. The process will stop at that AOD value (at 550 nm), for which the forecast electric power will match the real produced electric power by the photovoltaic panel within a previously defined threshold. This proof of concept is the first step of a wider project that aims to develop a user-friendly smartphone application where photovoltaic panel owners, once downloaded it on a voluntary basis, can turn their photovoltaic system into a sunphotometer to continuously retrieve the AOD, and more importantly, to monitor the air quality and detect strong air pollution episodes that pose a threat for population health.

Effect of Varying Aerosol Concentrations and Relative Humidity on Visibility and Particle Size Distribution in Urban Atmosphere

Atmospheric aerosol concentrations have been found to change constantly due to the influence of source, winds and human activities over short time periods. This has proved to be a constraint to the study of varied aerosol concentrations in urban atmosphere alongside changing relative humidity and how it affects visibility and aerosol particle size distribution. In this research simulation was carried out using Optical Properties of Aerosols and Clouds (OPAC 4.0) average concentration setup for relative humidity (RH) 0-99% at visible wavelength 0.4-0.8 μm to vary the concentrations of three aerosol components: WASO (Water-soluble), INSO (Insoluble) and SOOT. The Angstrom exponents (α), the curvatures (α2) and atmospheric turbidities (β) were obtained from the regression analysis of Kaufman’s first and second order polynomial equations for visibility. The research determined the mean exponent of the aerosol size growth curve (µ) from the effective hygroscopic growth (geff) and the humidification factors (γ) from visibility enhancement f (RH, λ). The mean exponent of aerosol size distributions (υ) was determined from µ and γ. The results showed that with varied WASO, INSO and SOOT concentrations respectively at different RH, aerosol particle size distributions showed bimodal characteristics with dominance of fine mode particles. Hazy atmospheric conditions prevailed with increasing turbidity.

Spatial–temporal analysis of urban ecological comfort index derived from remote sensing data: a case study of Hefei, China

In recent years, ecological and environmental degradations have occurred widely around the world, especially in urban areas with rapid economic development, intensive human activities, and massive urbanization. We aim at proposing an effective and comprehensive urban ecological environment evaluation method based on remote sensing. Harmonic analysis of time series method and spatial–temporal information fusion based on nonlocal means filter was used to improve the quality of long-term remote sensing data from 2003 to 2019. The greenness, dryness, heat, and atmospheric turbidity of the ecological environment were represented by normalized difference vegetation index, normalized difference built-up index, land surface temperature, and aerosol optical depth, respectively. An urban ecological comfort index (UECI) was established by the weighted combination of the above four indicators using entropy method. UECI was calculated at seasonal and annual scales from 2003 to 2019 in Hefei city, China, as a case study. Furthermore, Mann–Kendall trend test, spatial autocorrelation analysis, and spatial clustering analysis were performed on the UECI results. The experimental results show that UECI in Hefei had an increasing trend from 0.5446 to 0.5737 between 2003 and 2019, indicating that its ecological environment is actively improving. UECI is roughly equal in spring, summer, and autumn, and the worst is in winter. The responses of UECI to urban expansion, environmental protection policies, and extreme weather were also explored. Its sensitively analysis results and the fact that the spatial–temporal trends of UECI were consistent with the influencing factors reflected the stability and reliability of UECI, indicating that the proposed UECI performed well in Hefei and could be applied to measure urban ecological comfort level in other study areas.

Study of Atmospheric Turbidity in a Northern Tropical Region Using Models and Measurements of Global Solar Radiation

Radiative transfer in the Earth’s atmosphere under clear-sky conditions strongly depends on turbidity due to aerosols and hydrometeors. It is therefore important to know its temporal radiative properties for a given site when the objective is to optimize the solar energy that is collected there. Turbidity can be studied via measurements and models of the global solar radiation reaching the ground in cloudless conditions. These models generally depend on two parameters, namely the Angström turbidity coefficient and the Linke factor. This article aims to do a comparative study of five models of global solar radiation, all dependent on the Linke factor, based on real data. The measurements are provided by the Tamanrasset Meteorological Center (Algeria), which has a long series of global solar radiation data recorded between 2005 and 2011. Additional data from AERONET and MODIS onboard the TERRA satellite were also used to perform the comparison between the two estimated parameters and those obtained from AERONET. The study shows that the ESRA models are the most reliable among the five models for estimating the Linke factor with a correlation coefficient R of the data fits of 0.9995, a RMSE of 13.44 W/m2, a MBE of −0.64 W/m2 and a MAPE of 6.44%. The maximum and minimum statistical values were reached, respectively, in June and during the autumn months. The best correlation is also observed in the case of ESRA models between the Linke parameter and the joint optical thickness of aerosols and the total column-integrated water vapor. The Angström turbidity coefficient β, calculated from the Linke factor and MODIS data, has values less than 0.02 at 9% of the cases, and 76% present values ranging between 0.02 and 0.15 and 13% higher than 0.15. These β values are validated by AERONET measurements since a very good correlation (R≈0.87) is observed between the two datasets. The temporal variations of β also show a maximum in June. Satellite observations confirm more aerosols during the summer season, which are mostly related to the African monsoon.

Long-term variation in aerosol lidar ratio in Shanghai based on Raman lidar measurements

Abstract. Accurate lidar ratio (LR) and better understanding of its variation characteristics can not only improve the retrieval accuracy of parameters from elastic lidar, but also play an important role in assessing the impacts of aerosols on climate. Using the observational data of a Raman lidar in Shanghai from 2017 to 2019, LRs at 355 nm were retrieved and their variations and influence factors were analyzed. Within the height range of 0.5–5 km, about 90 % of the LRs were distributed in 10–80 sr with an average value of 41.0 ± 22.5 sr, and the LR decreased with the increase in height. The volume depolarization ratio (δ) was positively correlated with LR, and it also decreased with the increase in height, indicating that the vertical distribution of particle shape was one of the influence factors of the variations in LR with height. LR had a strong dependence on the original source of air masses. Affected by the aerosols transported from the northwest, the average LR was the largest, 44.2 ± 24.7 sr, accompanied by the most irregular particle shape. The vertical distribution of LR was affected by atmospheric turbidity, with the greater gradient of LR under clean conditions. The LR above 1 km could be more than 80 sr, when Shanghai was affected by biomass burning aerosols.

The Relationship between the Ultraviolet Radiation and Meteorological Factors and Atmospheric Turbidity: Part II. Role of Surface Albedo

The Relationship between the Ultraviolet Radiation and Meteorological Factors and Atmospheric Turbidity: Part II. Role of Surface Albedo

A calculation method of precise direct normal irradiance based on measured CSR

Radiation from the circumsolar region called circumsolar radiation is part of direct solar radiation, but not all circumsolar radiation can be received by concentrating solar power (CSP). Circumsolar ratio (CSR) reflects the proportion of circumsolar normal irradiance in direct normal irradiance (DNI). CSR is related to the radial distribution of solar brightness (sunshape) and it is influenced by atmospheric turbidity and air mass (AM).Performance of CSP is affected by CSR, which should be considered in the CSP simulation and analysis. This work introduces a method of measuring CSR with a CCD camera and filters. Also a calculation method of DNI for different field of view based on the measured CSR is proposed.